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Random forest and WiFi fingerprint-based indoor location recognition system using smart watch

Human-centric computing and information sciences, 2019-02, Vol.9 (1), p.1-14, Article 6 [Peer Reviewed Journal]

The Author(s) 2019 ;Human-centric Computing and Information Sciences is a copyright of Springer, (2019). All Rights Reserved. © 2019. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2192-1962 ;EISSN: 2192-1962 ;DOI: 10.1186/s13673-019-0168-7

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Title:
Random forest and WiFi fingerprint-based indoor location recognition system using smart watch
Author: Lee, Sunmin ; Kim, Jinah ; Moon, Nammee
Subjects: Artificial Intelligence ; Big data ; Biometric recognition systems ; Bluetooth ; Cloud Computing for Human-centric Computing ; Communications Engineering ; Computer Science ; Computer Systems Organization and Communication Networks ; Fingerprints ; Information Systems and Communication Service ; Information Systems Applications (incl.Internet) ; IoT ; Location based services ; Networks ; Radio frequency identification ; Signal strength ; Smartphones ; Smartwatches ; User Interfaces and Human Computer Interaction ; Wireless access points
Is Part Of: Human-centric computing and information sciences, 2019-02, Vol.9 (1), p.1-14, Article 6
Description: Various technologies such as WiFi, Bluetooth, and RFID are being used to provide indoor location-based services (LBS). In particular, a WiFi base using a WiFi AP already installed in an indoor space is widely applied, and the importance of indoor location recognition using deep running has emerged. In this study, we propose a WiFi-based indoor location recognition system using a smart watch, which is extended from an existing smartphone. Unlike the existing system, we use both the Received Signal Strength Indication (RSSI) and Basic Service Set Identifier (BSSID) to solve the problem of position recognition owing to the similar signal strength. By performing two times of filtering, we want to improve the execution time and accuracy through the learning of random forest based location awareness. In an unopened indoor space with five or more WiFi APs installed. Experiments were conducted by comparing the results according to the number of data for supposed system and a system based on existing WiFi fingerprint based random forest. The proposed system was confirmed to exhibit high performance in terms of execution time and accuracy. It has significance in that the system shows a consistent performance regardless of the number of data for location information.
Publisher: Berlin/Heidelberg: Springer Berlin Heidelberg
Language: English
Identifier: ISSN: 2192-1962
EISSN: 2192-1962
DOI: 10.1186/s13673-019-0168-7
Source: Springer Nature OA/Free Journals
ROAD: Directory of Open Access Scholarly Resources
ProQuest Central

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Random forest and WiFi fingerprint-based indoor location recognition system using smart watch

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